International Journal of Electrical, Electronics and Computer Systems (IJEECS)
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A Survey on Discovering Time Dependent Shortest Path in WSN
1
Pradhnya S. Kalaskar, 2Prachi Jaini
Department of Computer Science & Engineering
G. H. Raisoni College of Engineering, Nagpur-16,India
Email : 1pradhnyakalaskar@gmail.com, 2jaini14prachi@gmail.com
Abstract— The problem of least-latency end-to-end
multicast routing over adaptively duty-cycled wireless
sensor networks is considered. These type of networks
exhibit a time-dependent feature, where transmission
latency and link cost from one node to other nodes vary
constantly in different discrete time moments. It presents
distributed algorithm for finding the time dependent
shortest paths to the sink node for all nodes. It presents
distributed shortest path maintenance algorithms with low
message complexity and space complexity in wireless mesh
network .
Keywords- Duty cycle, Time dependent, ² synchronizer
I. INTRODUCTION
The transport and network layer protocols we have
studied so far provide for the delivery of packets from a
one source to a one destination. Protocols involving just
one sender and one receiver are often referred to as
unicast protocols. A number of network applications
which are come into view require the delivery of packets
from one or more senders to a group of receivers. These
applications include bulk data transmission, sharing of
data applications, data feeds, and interactive gaming. For
each of these applications, a widely useful abstraction is
the notion of a multicast: the sending of a packet from one
sender to multiple receivers with a single “transmit”
operation.
Multihop data routing over wireless sensor networks
(WSNs) has attracted extensive attention in the recent
years. Since there is no infrastructure in sensor networks,
the problem of routing is different from the one in
traditional wired networks or the Internet [1], [2]. Due to
the increasing interest in the dynamic management of
transmission systems, there is need to find shortest paths
over a large graph, where the weights (or delays)
associated with edges dynamically change over time (time
dependency). Transportation systems, which can provide
real-time traffic information (used to calculate edge
delays) to users, include the Vehicle Information and
Communication System (VICS) and the European Traffic
Message Channel2 (TMC) [3]. Here we study the
generalized form of this query; this problem is called
time-dependent shortest-path: to find the optimal path
(with the minimum travel time) from a source to a
destination, on a time-dependent graph, when the starting
time (departure time from the source) is selected from a
user-given starting-time interval. TDSP problem was
studied to find out approximate answers with
discrete-time approaches or with continuoustime
approaches [3].
Wireless sensor networks have been envisioned to be
useful in many military and civilian applications such as
battlefield habitat monitoring, surveillance, target
tracking, etc. Sensor nodes are generally powered by
battery hence techniques to prolong the network lifetime
have become the recent research focus. A variety of
energy conservation strategies have been proposed.
Among them, a frequently used mechanism is to deploy
more sensors than required, and schedule the activity of
each sensor node such that sensors perform the given
mission in turn and at any time, only a small number of
sensors are active to meet the coverage requirement of the
mission [4].
The focus on extremely low duty-cycle sensor networks,
in which energy management protocols aggressively
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International Journal of Electrical, Electronics and Computer Systems (IJEECS)
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reduce energy consumption by allowing a very low duty
cycle for each particular sensor device. During the
operation of sensor applications, sensor nodes are
activated and stay in sleep state for a long time. Due to the
extremely limited power, it is not feasible to maintain a
ready-to-use communication backbone continuously. For
any given time frame, this type of networks may be
fragmented (partitioned) and network connectivity
(topology) becomes time-dependent [5].
In other words, when a node has packets ready to be sent,
all of its neighbouring nodes may be in the dormant state
and the sender may have to wait for one of its neighbours
to wake up in order to forward its packet. The time spent
on waiting for a neighbour to wake up at the sender is
called sleep latency. Therefore, the sleep latency
dominates the E2E delay in such extremely low
duty-cycle sensor networks [5].
The problem of updating efficiently all-pairs shortest
paths in a distributed network whose topology
dynamically changes over the time, in the sense that link
weights can be modified during the lifetime of the
network, is considered crucial in today’s practical
applications. Hence, it is very important to find efficient
dynamic distributed algorithms for shortest paths [1], [6].
The Cyclic Quorum System Pair (CQSPair) which
contains a pair of quorum systems suitable for
heterogeneous quorum-based wakeup schedule. The
mechanism of CQS-Pair can guarantee that two adjacent
nodes adopt heterogeneous quorums. Wireless sensor
networks can achieve better trade-off between energy
consumption and average delay with the help of the
CQS-Pair Consider an example, all cluster- heads and
gateway nodes can pick up a quorum from the quorum
system with shorter cycle length as their wake up
schedule, to get shorter discovery delay. All members in a
cluster can choose a quorum from the system with longer
cycle length as their wakeup schedules, in order to save
more idle energy [7].
II. RELATED WORK
In [1] Shouwen Lai and Binoy Ravindran chose two MAC
protocols: ALPL and quorum-based duty-cycling. In the
ALPL mode, a node just wakes up for a short time during
a checking interval to check the channel activities. The
duration of the checking interval varies for different
nodes. They changed the duration of the checking interval
in their simulation experiments with four sets, C1, C2, C3,
and C4. With each set, they randomly chose one element
as the value of the LPL checking interval for each node.
With different time slot sets, the size of a message is
changing. Thus, they used a flexible packet size in their
simulation. Each element in a vector occupied 1 byte in all
experiments. For quorum-based dutycycling, they choose
the (7,3,1) and (21,5,1) difference sets for the
heterogeneous wakeup schedule settings. The duration of
one time slot was set to 100 ms in quorum-based
dutycycling. Since FTSP, FTSP-M, TD-Bellman, and
DSPP1 are independent of wakeup scheduling, we argue
that the comparison is fair even when we choose
quorum-based dutycycling.
LPL/ALPL in WSNs: LPL means that a node only wakes
up and listens the channel state for a short time period. For
example it include B-MAC which is a CSMA-based
technique utilizing low power listening and an extended
preamble to achieve low power communication. In
B-MAC, nodes have an independent sleep-wake up
schedule. In BMAC if a node wishes to transmit the
packet, it precedes the data packet with a preamble that is
slightly longer than the sleep period of the receiver. A
node samples the medium during the awake period and if
a preamble is detected, it remains awake to receive the
data. Using the extended preamble, a sender is assured
that at some point during the preamble, the receiver will
wake up, then detect the preamble, and remain awake in
order to receive the data. B-MAC surpasses existing
protocols in terms of energy consumption, latency and
throughput. B-MAC performs quite well but it suffers
from the overhearing problem, therefore the long
preamble dominates the energy usage [1].
Delay-efficient routing over adaptively duty-cycled
WSNs: The adaptively duty-cycled WSNs, routing will be
more difficult due to two reasons: (1) intermittent
connection between two neighbour nodes and (2) changes
in the transmission latency at different times. In recent
year some work has studied the delay-efficient routing
problem over adaptively duty-cycled WSNs [1]. Su et al.
[4] proposed two methods to solve routing over
intermittently connected WSNs for duty cycling. One is
by an on-demand approach; it uses probe messages to
determine the least-latency route. And the second method
is called as proactive method, where all leastlatency
routes at different departure times are computed at the
beginning. The first method does not work properly for
frequent data deliveries. The other one is a centralized
approach which is not flexible for distributed
construction. Time-dependent shortest path problem was
first proposed by Cooke and Halsey [16]. It has been well
studied in the field of traffic network time dependent
graphs, and GPS navigation. Some previous solutions for
this problem mostly work offline using a centralized
approach. These solutions cannot be applied to WSNs
where the global network topology is not known by a
centralized node. The work in [17] discusses two
techniques for the time dependent shortest path problem:
waiting and no waiting. Waiting does not mean waiting in
the buffer, but it means that waiting for some time after the
data has been delivered (i.e., the receiver is awake). No
waiting means that a sender will immediately send the
data once the receiver is awake. In [17] they do not
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International Journal of Electrical, Electronics and Computer Systems (IJEECS)
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consider the waiting policy in their work, since the
end-to-end latency does not benefit from waiting.
Dynamic shortest path maintenance: Work in [18], exist
for handling the dynamically link decreases and increases,
and node insertion and deletion in static networks. In [19],
an algorithm is given for computing all-pairs shortest
paths, which requires O(n2) messages when the network
size is n. In [20], Ramarao and Venkatesan give a solution
for updating all-pairs shortest paths that requires O(n3)
messages, O(n3) time, and O(n) space. In the worst case,
updating the shortest path is as not easy as computing
shortest paths. They suggest two possible ideas toward
devising efficient fully dynamic algorithms for updating
all-pairs shortest paths: 1) explore the trade-off between
the messages, time and space complexity for each kind of
dynamic change, and 2) devise algorithms that are
efficient in different complexity models.
stores the route entries for all other nodes, the algorithms
in [18] are also memory-inefficient, incurring the space
complexity of O(|V|). Unlike previous works in [18],
where each node stores the route information for all other
nodes, the [1] propose a solution in which a node only
stores the route information to the sink node, which is
more practical in WSNs due to their memory constraints.
In the paper [1] they proposed algorithm, when one node
is updated (denoted as the source node), the algorithm
does not update the shortest path for the whole network
from scratch, but it updates the information of necessary
nodes. Thus, the main idea is first to identify which nodes
need to be updated. After that, the algorithm updates the
shortest path for these identified nodes. The below table I
shows the summary of related work.
TABLE
Summary of Related work
 -Synchronizer: The synchronizer is a methodology for
designing
efficient
distributed
algorithms
in
asynchronous networks. Many researchers have used
synchronizers to reduce message complexity of some
asynchronous algorithms, for example, Bellman-Ford
algorithm. A synchronizer works as follows: a
synchronizer generates sequences of “clockpulses” at
each node of a network. A new pulse is generated at each
node only after it receives all the messages which were
sent to that node by its neighbours at the previous pulse.
Hence, a synchronizer runs in a phase-by- phase manner
[1], [21].
A  -synchronizer is a special type of synchronizer, it has
an initialization phase includes a leader s’ is chosen in the
network and a spanning tree rooted at s’ is constructed.
After the execution of one phase, the leader s’ will
eventually learn that all the nodes in the network are
“safe.” simultaneously, s’ broadcasts a message along the
spanning tree, notifying all the nodes that they may
generate a new pulse. The pattern for communication to
receive all acknowledgments is just like convergecast.
With a  -synchronizer, whenever a node learns that it is
safe and all its descendants in the tree are safe, and if there
are no any defective nodes, it sends an acknowledgment to
its parent [1], [21]. The proposed distributed algorithms
for path maintenance are also equipped with the
 -synchronizer in order to avoid exponential message
complexity. The proposed algorithms in [1], referred to as
FTSP-M (Fast Time Dependent Shortest PathMaintenance), which is used to focus on per-node update
and can be easily extended to node deletion and insertion.
The algorithms will run concurrently at multiple nodes if
there are multiple node updates. Some previous works in
static networks in [18] have proposed solutions that
efficiently deal with single link updates. Since each node
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International Journal of Electrical, Electronics and Computer Systems (IJEECS)
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III. CONCLUSION
[10]
In this paper we have studied the various shortest path
algorithms for time dependency in wireless sensor
network. Also studied the distributed shortest path routing
problem in duty cycled wireless sensor network using
adaptive duty cycle and ² synchronizer for decreasing the
time, space and message complexity of a node.
W. Ye, J. Heidemann, and D. Estrin, “Medium
Access Control With Coordinated Adaptive
Sleeping for Wireless Sensor Networks,” In
IEEE/ACM Transaction 2004.
[11]
T.V. Dam and K. Langendoen, “An Adaptive
Energy-Efficient Mac Protocol for Wireless
Sensor Networks,” in ACM 2003.
IV. REFERENCES
[12]
M. Zuniga and B. Krishnamachari, “Analyzing the
Transitional Region in Low Power Wireless
Links,” in IEEE 2004.
[13]
S. Lai and B. Ravindran, “On Distributed
Time-Dependent Shortest Paths over Duty-Cycled
Wireless Sensor Networks,” in IEEE INFOCOM,
2010.
C. Schurgers and M.B. Srivastava,“Energy
Efficient Routing in Wireless Sensor Networks,”
2001.
[14]
A. Woo, T. Tong, and D. Culler, “Taming the
Underlying Challenges of Reliable Multihop
Routing in Sensor Networks,” 2003.
[3]
B. Ding, J.X. Yu, and L. Qin, “Finding
Time-Dependent Shortest Paths over Large
Graphs,” Proc. 11th Int’l Conf. Extending
DatabaseTechnology (EDBT ’08), pp. 205-216,
2008.
[15] C.M. Vigorito, D. Ganesan, and A.G. Barto,
“Adaptive Control of Duty Cycling in
Energy-Harvesting Wireless Sensor Networks,”
2007.
[4]
L. Su, C. Liu, H. Song, and G. Cao, “Routing in
Intermittently Connected Sensor Networks,” in
IEEE , 2008.
[5]
Y. Gu, T. He, M. Lin, and J. Xu, “Spatiotemporal
Delay Control for Low-Duty-Cycle Sensor
Networks,” in IEEE, 2009.
[6]
G.F. Italiano, “Distributed Algorithms for
Updating Shortest Paths (Extended Abstract),”
Proc. Fifth Int’l Workshop Distributed Algorithms
2000.
[7]
S.Lai, B. Zhang, B. Ravindran, and H.Cho,
“CQS-Pair: Cyclic Quorum System Pair for
Wakeup Scheduling in Wireless Sensor
Networks,” 2008.
[8]
[9]
[1]
[2]
S. Lai and B. Ravindran,“Least-Latency Routing
over Time Dependent Wireless Sensor Networks,”
in IEEE transaction 2013.
[16]
K.L. Cooke and E. Halsey, “The Shortest Route
through a Network with Time-Dependent
Internodal Transit Times,” J. Math. Analysis
Application 1966.
[17]
A. Orda and R. Rom, “Distributed Shortest-Path
Protocols for Time- Dependent Networks,”
Distributed Computing, 1996.
[18]
C. Serafino, D. Gabriele, F. Daniele, and N.
Umberto, “A Fully Dynamic Algorithm for
Distributed Shortest Paths,” Theoretical Computer
Science, 2003.
[19]
S. Haldar, “An “All Pairs Shortest Paths”
Distributed Algorithm Using 2n2 Messages,” J.
Algorithms, 1997.
P. Sommer and R. Wattenhofer, “Gradient Clock
Synchronization in Wireless Sensor Networks,” In
ACM 2009.
[20]
K.V.S. Ramarao and S. Venkatesan, “On Finding
and Updating Shortest Paths Distributively,” J.
Algorithms, 1992.
Y.Sun, O. Gurewitz, and D.B. Johnson, “RI-MAC:
A Receiver Initiated Asynchronous Duty Cycle
MAC Protocol for Dynamic Traffic Loads in
Wireless Sensor Networks,” In ACM, 2008.
[21]
B. Awerbuch, “Complexity
Synchronization,” J. ACM, 1985.
of
Network
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